Image analysis and interpretation contribute to many medical (e.g., detecting diseased tissues), surveillance (e.g., forest fire detection), and industrial applications (e.g., detecting manufacturing defects). All application scenarios require that the human observer have minimum level of proficiency in analyzing the relevant images. It is observed that requisite experience increases proficiency in image analysis. The knowledge responsible for this improvement is gathered through practice and interaction with other domain experts. However, access to experts is limited to very few persons of a field due to factors such as remoteness, or lack of an established local program in the relevant field. This limits the ability of an inexperienced person to learn the necessary knowledge, which in turn can have serious consequences in some fields such as medical pathology detection, forest fire detection or identifying drought/flood risks from satellite images.
Existing virtual reality systems and simulated systems that provide teaching may be limited to cases that are part of the training data set diagnosed by an expert, and may not be able to cope with new images that present unseen scenarios, features, and/or characteristics. Those systems also do not exploit image features as part of a teaching process.